A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf
.
Particle Filter Based Probabilistic Forced Alignment for Continuous Gesture Recognition
2017
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
In this paper, we propose a novel particle filter based probabilistic forced alignment approach for training spatiotemporal deep neural networks using weak border level annotations. The proposed method jointly learns to localize and recognize isolated instances in continuous streams. This is done by drawing training volumes from a prior distribution of likely regions and training a discriminative 3D-CNN from this data. The classifier is then used to calculate the posterior distribution by
doi:10.1109/iccvw.2017.364
dblp:conf/iccvw/CamgozHB17
fatcat:rvxu7i3xinekfayrigpqoxagya